Update README.md

README.md

@@ -1,97 +1,97 @@
----
-license: apache-2.0
-base_model: Helsinki-NLP/opus-mt-ar-en
-tags:
-- machine-translation
-- marian
-- mcwc
-- legal-nlp
-- constitutional-texts
-- generated_from_keras_callback
-model-index:
-- name: marian-finetuned-mcwc-ara-to-en
-  results: []
----
-
-# Marian MT fine-tuned on the Multilingual Corpus of World’s Constitutions (MCWC)
-This model is a fine-tuned version of [Helsinki-NLP/opus-mt-es-en](https://huggingface.co/Helsinki-NLP/opus-mt-es-en), adapted using high-quality sentence-aligned constitutional text from the **Multilingual Corpus of World’s Constitutions (MCWC)**  
-📄 MCWC paper (OSACT 2024): https://aclanthology.org/2024.osact-1.7/
-
-**This variant handles: _Arabic → English_ translation.**
-
----
-
-## Overview
-The MCWC provides a curated multilingual collection of constitutional texts from countries across the world. The corpus emphasises data cleanliness, high-quality sentence alignment, and detailed metadata (including country and continent mappings). It supports research in:
-
-- legal and constitutional NLP  
-- comparative constitutional studies  
-- multilingual machine translation  
-- cross-lingual semantic analysis  
-
-This model was fine-tuned on the Arabic-English segment of the MCWC, enabling translation that is more attuned to legal and constitutional language than general-purpose MT systems.
-
----
-
-## Intended use
-This model is suitable for tasks such as:
-
-- translating constitutional or legal documents  
-- cross-lingual legal text comparison  
-- multilingual information extraction  
-- downstream legal NLP tasks requiring domain-specific MT  
-
-It is **not** intended for casual or conversational translation, as it is optimised for formal and legal text.
-
----
-
-## Training data
-The model was trained on the MCWC’s Arabic-English aligned sentence pairs.  
-The MCWC dataset includes:
-
-- cleaned constitutional text  
-- high-quality sentence segmentation  
-- pairwise alignments  
-- country and regional metadata  
-
-More details may be found in the accompanying paper:
-> *El-Haj, M. & Ezzini, S. (2024). “The Multilingual Corpus of World’s Constitutions (MCWC).”*  
-> OSACT @ LREC-COLING 2024.  
-> https://aclanthology.org/2024.osact-1.7/
-
----
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 384, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
-- training_precision: mixed_float16
-
-### Training results
-
-| Train Loss | Validation Loss | Epoch |
-|:----------:|:---------------:|:-----:|
-| 1.3918     | 1.1473          | 0     |
-| 1.0745     | 1.1021          | 1     |
-| 0.9486     | 1.0908          | 2     |
-
-
-### Framework versions
-
-- Transformers 4.33.3
-- TensorFlow 2.13.0
-- Datasets 2.14.5
-- Tokenizers 0.13.3
-
----
-
-## Citation
-
-If you use this model, please cite the MCWC paper:
-
-**El-Haj, M. & Ezzini, S. (2024).**  
-*The Multilingual Corpus of World’s Constitutions (MCWC).*  
-Proceedings of OSACT @ LREC-COLING 2024.  
+---
+license: apache-2.0
+base_model: Helsinki-NLP/opus-mt-ar-en
+tags:
+- machine-translation
+- marian
+- mcwc
+- legal-nlp
+- constitutional-texts
+- generated_from_keras_callback
+model-index:
+- name: marian-finetuned-mcwc-ara-to-en
+  results: []
+---
+
+# Marian MT fine-tuned on the Multilingual Corpus of World’s Constitutions (MCWC)
+This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en), adapted using high-quality sentence-aligned constitutional text from the **Multilingual Corpus of World’s Constitutions (MCWC)**  
+📄 MCWC paper (OSACT 2024): https://aclanthology.org/2024.osact-1.7/
+
+**This variant handles: _Arabic → English_ translation.**
+
+---
+
+## Overview
+The MCWC provides a curated multilingual collection of constitutional texts from countries across the world. The corpus emphasises data cleanliness, high-quality sentence alignment, and detailed metadata (including country and continent mappings). It supports research in:
+
+- legal and constitutional NLP  
+- comparative constitutional studies  
+- multilingual machine translation  
+- cross-lingual semantic analysis  
+
+This model was fine-tuned on the Arabic-English segment of the MCWC, enabling translation that is more attuned to legal and constitutional language than general-purpose MT systems.
+
+---
+
+## Intended use
+This model is suitable for tasks such as:
+
+- translating constitutional or legal documents  
+- cross-lingual legal text comparison  
+- multilingual information extraction  
+- downstream legal NLP tasks requiring domain-specific MT  
+
+It is **not** intended for casual or conversational translation, as it is optimised for formal and legal text.
+
+---
+
+## Training data
+The model was trained on the MCWC’s Arabic-English aligned sentence pairs.  
+The MCWC dataset includes:
+
+- cleaned constitutional text  
+- high-quality sentence segmentation  
+- pairwise alignments  
+- country and regional metadata  
+
+More details may be found in the accompanying paper:
+> *El-Haj, M. & Ezzini, S. (2024). “The Multilingual Corpus of World’s Constitutions (MCWC).”*  
+> OSACT @ LREC-COLING 2024.  
+> https://aclanthology.org/2024.osact-1.7/
+
+---
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 384, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
+- training_precision: mixed_float16
+
+### Training results
+
+| Train Loss | Validation Loss | Epoch |
+|:----------:|:---------------:|:-----:|
+| 1.3918     | 1.1473          | 0     |
+| 1.0745     | 1.1021          | 1     |
+| 0.9486     | 1.0908          | 2     |
+
+
+### Framework versions
+
+- Transformers 4.33.3
+- TensorFlow 2.13.0
+- Datasets 2.14.5
+- Tokenizers 0.13.3
+
+---
+
+## Citation
+
+If you use this model, please cite the MCWC paper:
+
+**El-Haj, M. & Ezzini, S. (2024).**  
+*The Multilingual Corpus of World’s Constitutions (MCWC).*  
+Proceedings of OSACT @ LREC-COLING 2024.  
 https://aclanthology.org/2024.osact-1.7/